Yasmine Guendouz

Title: Using Machine Learning to Identify the critical features of carotid artery plaque vulnerability from Ultrasound Images

Supervision Team: Caitríona Lally, TCD / Catherine Mooney, UCD

Description: Over one million people in Europe have severe carotid artery stenosis, which may rupture causing stroke, the leading cause of disability and the third leading cause of death in the Western World. This project aims to develop a validated means of assessing the predisposition of a specific plaque to rupture using Ultrasound (US) imaging. Using machine learning (ML) techniques, we will look at multiple US modalities concomitantly; including B-mode images, elastography and US imaging with novel contrast agents for identifying neovascularisation. Combining this with in silico modelling of the vessels will provide us with a unique capability to verify the clinical and US findings by looking at the loading on the plaques and therefore the potential risk of plaque rupture. Proof of the diagnostic capabilities of ML and non-invasive, non-ionising US imaging in vivo for the diagnosis of vulnerable carotid artery disease would be a ground-breaking advancement in early vascular disease diagnosis.